用语义类比法拆分复合词
Splitting Compounds by Semantic Analogy
摘要：
在某些语言中，复合是一种高生产力的构词过程，这对于自然语言处理应用程序来说常常是有问题的。在本文中，我们研究了词嵌入形式的分布语义是否可以实现更深层次的，即，比标准的基于字符串的方法更丰富的知识，处理化合物。我们提出了一种无监督的方法，利用语义向量空间（基于类比，如“书店是商店的书架是货架”），以产生高质量的复合分析。基于这些分析的后续化合物拆分算法非常有效，特别是对于不明确的化合物。德语到英语的机器翻译实验表明，这种基于语义类比的复合分割器比常用的基于频率的方法得到更好的翻译。
英文摘要：
Compounding is a highly productive word-formation process in some languages that is often problematic for natural language processing applications. In this paper, we investigate whether distributional semantics in the form of word embeddings can enable a deeper, i.e., more knowledge-rich, processing of compounds than the standard string-based methods. We present an unsupervised approach that exploits regularities in the semantic vector space (based on analogies such as "bookshop is to shop as bookshelf is to shelf") to produce compound analyses of high quality. A subsequent compound splitting algorithm based on these analyses is highly effective, particularly for ambiguous compounds. German to English machine translation experiments show that this semantic analogy-based compound splitter leads to better translations than a commonly used frequency-based method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
